{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.rcParams['figure.dpi'] = 300 \n",
    "import numpy as np\n",
    "\n",
    "x = np.arange(-20, 20, 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = 1.0/ (1 + np.exp(-x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y)\n",
    "plt.xlabel('z')\n",
    "plt.ylabel('delta(z)')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入pandas and numpy 工具包\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创造特征列表\n",
    "column_names = ['Sample code name','Clump Thickness','Uniformity of Cell Size',\n",
    "                'Uniformity of Cell Shape','Marginal Adhesion','Single Epithelial Cell Size',\n",
    "                'Bare Nuclei','Bland Chromatin','Normal Nuleoli','Mitoses','Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用pandas.read_csv函数从互联网读取指定数据\n",
    "data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data',\n",
    "                  names = column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample code name</th>\n",
       "      <th>Clump Thickness</th>\n",
       "      <th>Uniformity of Cell Size</th>\n",
       "      <th>Uniformity of Cell Shape</th>\n",
       "      <th>Marginal Adhesion</th>\n",
       "      <th>Single Epithelial Cell Size</th>\n",
       "      <th>Bare Nuclei</th>\n",
       "      <th>Bland Chromatin</th>\n",
       "      <th>Normal Nuleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000025</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002945</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1015425</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1016277</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1017023</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sample code name  Clump Thickness  Uniformity of Cell Size  \\\n",
       "0           1000025                5                        1   \n",
       "1           1002945                5                        4   \n",
       "2           1015425                3                        1   \n",
       "3           1016277                6                        8   \n",
       "4           1017023                4                        1   \n",
       "\n",
       "   Uniformity of Cell Shape  Marginal Adhesion  Single Epithelial Cell Size  \\\n",
       "0                         1                  1                            2   \n",
       "1                         4                  5                            7   \n",
       "2                         1                  1                            2   \n",
       "3                         8                  1                            3   \n",
       "4                         1                  3                            2   \n",
       "\n",
       "  Bare Nuclei  Bland Chromatin  Normal Nuleoli  Mitoses  Class  \n",
       "0           1                3               1        1      2  \n",
       "1          10                3               2        1      2  \n",
       "2           2                3               1        1      2  \n",
       "3           4                3               7        1      2  \n",
       "4           1                3               1        1      2  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 699 entries, 0 to 698\n",
      "Data columns (total 11 columns):\n",
      "Sample code name               699 non-null int64\n",
      "Clump Thickness                699 non-null int64\n",
      "Uniformity of Cell Size        699 non-null int64\n",
      "Uniformity of Cell Shape       699 non-null int64\n",
      "Marginal Adhesion              699 non-null int64\n",
      "Single Epithelial Cell Size    699 non-null int64\n",
      "Bare Nuclei                    699 non-null object\n",
      "Bland Chromatin                699 non-null int64\n",
      "Normal Nuleoli                 699 non-null int64\n",
      "Mitoses                        699 non-null int64\n",
      "Class                          699 non-null int64\n",
      "dtypes: int64(10), object(1)\n",
      "memory usage: 60.1+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     402\n",
       "10    132\n",
       "5      30\n",
       "2      30\n",
       "3      28\n",
       "8      21\n",
       "4      19\n",
       "?      16\n",
       "9       9\n",
       "7       8\n",
       "6       4\n",
       "Name: Bare Nuclei, dtype: int64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#发现Bare Nuclei列里面的特征数值是非数值（object）类型，因此需要仔细查验；统计一下这列特征的数值频率。\n",
    "data['Bare Nuclei'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample code name</th>\n",
       "      <th>Clump Thickness</th>\n",
       "      <th>Uniformity of Cell Size</th>\n",
       "      <th>Uniformity of Cell Shape</th>\n",
       "      <th>Marginal Adhesion</th>\n",
       "      <th>Single Epithelial Cell Size</th>\n",
       "      <th>Bare Nuclei</th>\n",
       "      <th>Bland Chromatin</th>\n",
       "      <th>Normal Nuleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.990000e+02</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>402</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.071704e+06</td>\n",
       "      <td>4.417740</td>\n",
       "      <td>3.134478</td>\n",
       "      <td>3.207439</td>\n",
       "      <td>2.806867</td>\n",
       "      <td>3.216023</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.437768</td>\n",
       "      <td>2.866953</td>\n",
       "      <td>1.589413</td>\n",
       "      <td>2.689557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.170957e+05</td>\n",
       "      <td>2.815741</td>\n",
       "      <td>3.051459</td>\n",
       "      <td>2.971913</td>\n",
       "      <td>2.855379</td>\n",
       "      <td>2.214300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.438364</td>\n",
       "      <td>3.053634</td>\n",
       "      <td>1.715078</td>\n",
       "      <td>0.951273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.163400e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.706885e+05</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.171710e+06</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.238298e+06</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.345435e+07</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sample code name  Clump Thickness  Uniformity of Cell Size  \\\n",
       "count       6.990000e+02       699.000000               699.000000   \n",
       "unique               NaN              NaN                      NaN   \n",
       "top                  NaN              NaN                      NaN   \n",
       "freq                 NaN              NaN                      NaN   \n",
       "mean        1.071704e+06         4.417740                 3.134478   \n",
       "std         6.170957e+05         2.815741                 3.051459   \n",
       "min         6.163400e+04         1.000000                 1.000000   \n",
       "25%         8.706885e+05         2.000000                 1.000000   \n",
       "50%         1.171710e+06         4.000000                 1.000000   \n",
       "75%         1.238298e+06         6.000000                 5.000000   \n",
       "max         1.345435e+07        10.000000                10.000000   \n",
       "\n",
       "        Uniformity of Cell Shape  Marginal Adhesion  \\\n",
       "count                 699.000000         699.000000   \n",
       "unique                       NaN                NaN   \n",
       "top                          NaN                NaN   \n",
       "freq                         NaN                NaN   \n",
       "mean                    3.207439           2.806867   \n",
       "std                     2.971913           2.855379   \n",
       "min                     1.000000           1.000000   \n",
       "25%                     1.000000           1.000000   \n",
       "50%                     1.000000           1.000000   \n",
       "75%                     5.000000           4.000000   \n",
       "max                    10.000000          10.000000   \n",
       "\n",
       "        Single Epithelial Cell Size Bare Nuclei  Bland Chromatin  \\\n",
       "count                    699.000000         699       699.000000   \n",
       "unique                          NaN          11              NaN   \n",
       "top                             NaN           1              NaN   \n",
       "freq                            NaN         402              NaN   \n",
       "mean                       3.216023         NaN         3.437768   \n",
       "std                        2.214300         NaN         2.438364   \n",
       "min                        1.000000         NaN         1.000000   \n",
       "25%                        2.000000         NaN         2.000000   \n",
       "50%                        2.000000         NaN         3.000000   \n",
       "75%                        4.000000         NaN         5.000000   \n",
       "max                       10.000000         NaN        10.000000   \n",
       "\n",
       "        Normal Nuleoli     Mitoses       Class  \n",
       "count       699.000000  699.000000  699.000000  \n",
       "unique             NaN         NaN         NaN  \n",
       "top                NaN         NaN         NaN  \n",
       "freq               NaN         NaN         NaN  \n",
       "mean          2.866953    1.589413    2.689557  \n",
       "std           3.053634    1.715078    0.951273  \n",
       "min           1.000000    1.000000    2.000000  \n",
       "25%           1.000000    1.000000    2.000000  \n",
       "50%           1.000000    1.000000    2.000000  \n",
       "75%           4.000000    1.000000    4.000000  \n",
       "max          10.000000   10.000000    4.000000  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将'?'替换为标准缺失值表示\n",
    "data = data.replace(to_replace='?',value=np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(699, 11)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 丢弃带有缺失值的数据(只要有一个维度有缺失)\n",
    "data = data.dropna(how='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(683, 11)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 输出data的数据量和维度\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    444\n",
       "4    239\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看一下肿瘤类别的代表数字，以及各个类别的样本数量。良性肿瘤（2）：444组样本；恶性肿瘤（4）：239组样本。\n",
    "data['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample code name</th>\n",
       "      <th>Clump Thickness</th>\n",
       "      <th>Uniformity of Cell Size</th>\n",
       "      <th>Uniformity of Cell Shape</th>\n",
       "      <th>Marginal Adhesion</th>\n",
       "      <th>Single Epithelial Cell Size</th>\n",
       "      <th>Bland Chromatin</th>\n",
       "      <th>Normal Nuleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.830000e+02</td>\n",
       "      <td>683.000000</td>\n",
       "      <td>683.000000</td>\n",
       "      <td>683.000000</td>\n",
       "      <td>683.000000</td>\n",
       "      <td>683.000000</td>\n",
       "      <td>683.000000</td>\n",
       "      <td>683.000000</td>\n",
       "      <td>683.000000</td>\n",
       "      <td>683.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.076720e+06</td>\n",
       "      <td>4.442167</td>\n",
       "      <td>3.150805</td>\n",
       "      <td>3.215227</td>\n",
       "      <td>2.830161</td>\n",
       "      <td>3.234261</td>\n",
       "      <td>3.445095</td>\n",
       "      <td>2.869693</td>\n",
       "      <td>1.603221</td>\n",
       "      <td>2.699854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.206440e+05</td>\n",
       "      <td>2.820761</td>\n",
       "      <td>3.065145</td>\n",
       "      <td>2.988581</td>\n",
       "      <td>2.864562</td>\n",
       "      <td>2.223085</td>\n",
       "      <td>2.449697</td>\n",
       "      <td>3.052666</td>\n",
       "      <td>1.732674</td>\n",
       "      <td>0.954592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.337500e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.776170e+05</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.171795e+06</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.238705e+06</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.345435e+07</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sample code name  Clump Thickness  Uniformity of Cell Size  \\\n",
       "count      6.830000e+02       683.000000               683.000000   \n",
       "mean       1.076720e+06         4.442167                 3.150805   \n",
       "std        6.206440e+05         2.820761                 3.065145   \n",
       "min        6.337500e+04         1.000000                 1.000000   \n",
       "25%        8.776170e+05         2.000000                 1.000000   \n",
       "50%        1.171795e+06         4.000000                 1.000000   \n",
       "75%        1.238705e+06         6.000000                 5.000000   \n",
       "max        1.345435e+07        10.000000                10.000000   \n",
       "\n",
       "       Uniformity of Cell Shape  Marginal Adhesion  \\\n",
       "count                683.000000         683.000000   \n",
       "mean                   3.215227           2.830161   \n",
       "std                    2.988581           2.864562   \n",
       "min                    1.000000           1.000000   \n",
       "25%                    1.000000           1.000000   \n",
       "50%                    1.000000           1.000000   \n",
       "75%                    5.000000           4.000000   \n",
       "max                   10.000000          10.000000   \n",
       "\n",
       "       Single Epithelial Cell Size  Bland Chromatin  Normal Nuleoli  \\\n",
       "count                   683.000000       683.000000      683.000000   \n",
       "mean                      3.234261         3.445095        2.869693   \n",
       "std                       2.223085         2.449697        3.052666   \n",
       "min                       1.000000         1.000000        1.000000   \n",
       "25%                       2.000000         2.000000        1.000000   \n",
       "50%                       2.000000         3.000000        1.000000   \n",
       "75%                       4.000000         5.000000        4.000000   \n",
       "max                      10.000000        10.000000       10.000000   \n",
       "\n",
       "          Mitoses       Class  \n",
       "count  683.000000  683.000000  \n",
       "mean     1.603221    2.699854  \n",
       "std      1.732674    0.954592  \n",
       "min      1.000000    2.000000  \n",
       "25%      1.000000    2.000000  \n",
       "50%      1.000000    2.000000  \n",
       "75%      1.000000    4.000000  \n",
       "max     10.000000    4.000000  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用sklearn.model_selection的train_test_split模板用于分割数据\n",
    "from sklearn.model_selection import train_test_split\n",
    "# 随机采样25%的数据用于测试，剩下的75%用于构建训练集合\n",
    "X_train, X_test, y_train, y_test = train_test_split(data[column_names[1:10]],data[column_names[10]],test_size=0.25,random_state=2019)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    332\n",
       "4    180\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查验训练样本的数量和类别分布\n",
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    112\n",
       "4     59\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查验测试样本的数量和类别分布\n",
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 从sklearn.preprocessing导入StandardScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# 从sklearn.linear_model导入LogisticRegression and SGDClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import SGDClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 标准化数据，保证每个维度的特征数据方差为1，均值为0.使得预测结果不会被某些维度过大的特征值主导\n",
    "ss = StandardScaler()\n",
    "X_train = ss.fit_transform(X_train)\n",
    "X_test = ss.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 初始化 LogisticRegression 与 SGDClassifier\n",
    "lr = LogisticRegression()\n",
    "sgdc = SGDClassifier()\n",
    "# 调用fit函数来训练模型参数\n",
    "lr.fit(X_train,y_train)\n",
    "# 用predict对X_test进行预测\n",
    "lr_y_predict = lr.predict(X_test)\n",
    "sgdc.fit(X_train,y_train)\n",
    "sgdc_y_predict= sgdc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "使用逻辑斯蒂回归模型做分类的准确率为: 0.970760\n",
      "Accuaracy of LR Classifier: 0.9707602339181286\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      Benign       0.98      0.97      0.98       112\n",
      "   Malignant       0.95      0.97      0.96        59\n",
      "\n",
      "    accuracy                           0.97       171\n",
      "   macro avg       0.97      0.97      0.97       171\n",
      "weighted avg       0.97      0.97      0.97       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 从sklearn.metrics 导入 classification_report 模块\n",
    "from sklearn.metrics import classification_report\n",
    "# 从sklearn.metrics导入accuracy_score，用来做分类准确率的评估。\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "print('%s: %f'%('使用逻辑斯蒂回归模型做分类的准确率为', accuracy_score(y_test, lr.predict(X_test))))\n",
    "print('Accuaracy of LR Classifier:',lr.score(X_test, y_test))\n",
    "print(classification_report(y_test,lr_y_predict,target_names = ['Benign','Malignant']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuaracy of SGD Classifier: 0.935672514619883\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      Benign       0.94      0.96      0.95       112\n",
      "   Malignant       0.93      0.88      0.90        59\n",
      "\n",
      "    accuracy                           0.94       171\n",
      "   macro avg       0.93      0.92      0.93       171\n",
      "weighted avg       0.94      0.94      0.94       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Accuaracy of SGD Classifier:',sgdc.score(X_test, y_test))\n",
    "print(classification_report(y_test,sgdc_y_predict,target_names = ['Benign','Malignant']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[109   3]\n",
      " [  2  57]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "con_mat = confusion_matrix(y_test, lr_y_predict, labels=[2,4])\n",
    "print(con_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.95\n",
      "0.9661016949152542\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import recall_score, precision_score, precision_recall_curve\n",
    "import numpy as np\n",
    "print(precision_score(y_test, lr_y_predict, pos_label=4))\n",
    "print(recall_score(y_test, lr_y_predict, pos_label=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_y_predict_prob = lr.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "precisions, recalls, thresholds = precision_recall_curve(y_test, np.max(lr_y_predict_prob, axis=1), pos_label=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f14e08940d0>]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.rcParams['figure.dpi'] = 300\n",
    "plt.plot(recalls, precisions)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
